We invite you to submit a paper to AIST’18, a scientific conference on Analysis of Images, Social Networks, and Texts. The conference is intended for researchers and practitioners interested in data science. Similarly to the previous year, the conference will be particularly focused on innovative applications of data mining and machine learning techniques to image processing, analysis of social networks, text processing and other domains, such as economics or geo information systems.

The conference is intended for computer scientists and practitioners whose research interests are related to data science. The previous conferences in 2012-2017 attracted a significant number of students, researchers, academics and engineers working on analysis of images, texts, and social networks. The broad scope of AIST makes it an event where researchers from different domains, such as image and text processing, exploiting various data analysis techniques, can meet and exchange ideas. The conference allows specialists from different fields to meet each other, present their work, and discuss both theoretical and practical aspects of their data analysis problems. Another important aim of the conference is to stimulate scientists and people from the industry to benefit from the knowledge exchange and identify possible grounds for fruitful collaboration.

Similarly to the previous years, the conference proceedings will be published in the Springer’s in Lecture Notes in Computer Science (LNCS) series.

Past Conferences

If you did not participate before in the AIST conference you can take a look at the proceedings of the last three years to get an idea about which kind of papers are accepted and which kinds of topics are discussed at the conference. Proceeding of the previous years can be found below:

Papers should be submitted through the EasyChair conference management system (see the link above). Submitted papers should be written in English and provide sufficient detail to allow the Program Committee to assess the merits of the paper on the basis of technical quality, relevance to the conference topics, originality, significance, and clarity of presentation. Papers in the Russian language are not accepted.

The program committee expects that authors are ready to submit high-quality research papers. We also require at least one of the authors to attend the conference to present their study. Papers should present original work previously not published or concurrently submitted to another conference or journal.

Each paper will be reviewed by at least three PC members. To ensure a fair assessment of the submissions, the review will be double-blind, so you need to make your paper anonymous (remove links to your personal pages, acknowledgments, affiliations, etc.). Your work will be rejected in case you did not anonymize properly your paper.

Track 6. Analysis of dynamic behavior through event data

This year, we have a track on analysis of dynamic behavior through event data. An analysis of big data, containing dynamic processes of systems’ executions and collaborations in a form of event logs, is a challenging research direction also known as process mining or business process intelligence. Techniques for constructing process models from event logs, finding log and model deviations, and enhancing pre-existing process models with additional information extracted from logs can significantly assist in understanding systems’ behavior. Papers presenting original process mining approaches as well as case studies in discovering and analyzing processes using event data are sought. The scope of the section includes but not limited to the following topics: Algorithms and approaches for the discovery of process models from event logs; Techniques for the discovery of social nets from communication logs; Representation and visualization of models discovered from event logs; Methods for finding deviations between real and expected system’s behavior; Complex event processing to assist process intelligence; Compliance management and conformance checking; Applying process mining techniques in various domains, such as e-government, healthcare, banking, manufacturing, booking systems and others.